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This thesis presents an integrated two-step learning framework that uses deep learning to simplify and increase the accuracy of the breast cancer diagnosis process. This innovative methodology differs from traditional methods by using raw, unmarked X-ray images, and this would facilitate the pre-treatment phase.

In her thesis, the researcher touched on the effect of honey samples depending on the type of blossoms. There were some effects on cell growth in the laboratory. Thus, these samples reduced cancer cell growth, inflammation, and oxidation, in addition to limiting the spread of cancer cells.